Modern industrial equipment are equipped with a large number of sensors that continuously monitor the behavior of component parts and sub-systems thereof. For example, industrial machines, including consumer and commercial vehicles, aircrafts, power plants and manufacturing plants generally instrumented with a large number of sensors that continuously transmit their readings wirelessly. Due to increasingly ubiquitous Internet connectivity, often via cellular as well as metropolitan Wifi networks, modern equipment of all kinds regularly transmit sensor readings to their manufacturers (e.g. automobile, engine, or component OEMs) as well as operators (e.g. airline, factories, power plants). The data transmitted by industrial equipment can be utilized to determine different usage patterns and behavior of similar machines.
The inventors here have recognized several technical problems with such conventional systems, as explained below. The sensor data from machines is high-dimensional in nature, and clustering such data to find patterns regarding machine is often complex. Additionally, currently the usage patterns of machines are visualized over a day, or, alternatively a continuous run, via a distribution of values taken by each of possibly dozens or even hundreds of sensors, usually visualized as histograms. The number of such histograms can often be in hundreds of thousands, therefore in order to succinctly summarize such days of operation into dominant patterns is complex.